U.S. patent application number 15/299893 was filed with the patent office on 2017-02-16 for system and methods for time deferred transmission of mobile data.
This patent application is currently assigned to The Trustees of Princeton University. The applicant listed for this patent is Mung Chiang, Sangtae Ha, Carlee Joe-Wong, Soumya Sen. Invention is credited to Mung Chiang, Sangtae Ha, Carlee Joe-Wong, Soumya Sen.
Application Number | 20170048395 15/299893 |
Document ID | / |
Family ID | 49004275 |
Filed Date | 2017-02-16 |
United States Patent
Application |
20170048395 |
Kind Code |
A1 |
Chiang; Mung ; et
al. |
February 16, 2017 |
System And Methods For Time Deferred Transmission Of Mobile
Data
Abstract
A system for time deferred usage of mobile data by a plurality
of user devices each coupled to a network server and running one or
more applications. The system includes a network measurement
module, user profiling module and a price-optimization
computational module located at the network server and a user
interface module located at each user device. The network
measurement module collects traffic data from each of the
applications to generate the historical congestion data and current
congestion data. The user profiling module is configured to receive
user reaction data and determine how much mobile data for each
application may be deferred to a later point in time to generate
the predicted user reaction data. The price-optimization receives
the historical congestion data and the current network congestion
data from the network measurement module and the predicted user
reaction data from the user profiling module and generate day-ahead
time-dependent price data for a plurality of upcoming timeslots of
mobile data based on the historical congestion data, current
network congestion, predicted user reaction data, network operator
costs for data exceeding maximum network capacity and network
operator costs for supplying data in less-congested time periods.
The user interface receives a time interval based delay selection
input for each of the one or more applications, select one or more
of the upcoming data timeslots of mobile data for use by the one or
more applications and delay mobile data usage by each of the one or
more applications based on the delay selection input.
Inventors: |
Chiang; Mung; (Princeton,
NJ) ; Joe-Wong; Carlee; (West Hills, CA) ; Ha;
Sangtae; (Princeton, NJ) ; Sen; Soumya;
(Princeton, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Chiang; Mung
Joe-Wong; Carlee
Ha; Sangtae
Sen; Soumya |
Princeton
West Hills
Princeton
Princeton |
NJ
CA
NJ
NJ |
US
US
US
US |
|
|
Assignee: |
The Trustees of Princeton
University
Princeton
NJ
|
Family ID: |
49004275 |
Appl. No.: |
15/299893 |
Filed: |
October 21, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13780941 |
Feb 28, 2013 |
|
|
|
15299893 |
|
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|
|
61604900 |
Feb 29, 2012 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04M 15/60 20130101;
G06Q 30/0206 20130101; H04M 15/58 20130101; H04M 15/8027
20130101 |
International
Class: |
H04M 15/00 20060101
H04M015/00 |
Claims
1. A system for time deferred usage of mobile data by a plurality
of user devices each coupled to a network server and running one or
more applications, wherein the system comprises: a network
measurement module located at the network server, a user profiling
module located at the network server and a price-optimization
computational module located at the network server and a user
interface module located at each user device, the network
measurement module being configured to collect traffic data from
each of the one or more applications to generate the historical
congestion data and current congestion data, the user profiling
module being configured to receive user reaction data and determine
how much mobile data for each application may be deferred to a
later point in time to generate the predicted user reaction data,
the price-optimization computational module being configured to
receive the historical congestion data and the current network
congestion data from the network measurement module and the
predicted user reaction data from the user profiling module and
generate day-ahead time-dependent price data for a plurality of
upcoming timeslots of mobile data based on the historical
congestion data, current network congestion, predicted user
reaction data, network operator costs for data exceeding maximum
network capacity and network operator costs for supplying data in
less-congested time periods, the user interface module being
configured receive a time interval based delay selection input for
each of the one or more applications, select one or more of the
upcoming data timeslots of mobile data for use by the one or more
applications and delay mobile data usage by each of the one or more
applications based on the delay selection input.
2. The system of claim 1, wherein the user interface module is
configured to display the time-dependent price data so that a user
can generate the delay selection input.
3. The system of claim 1 wherein the user interface module is
configured provide the time-dependent price data to an automatic
agent acting on behalf of the user so that the automatic agent can
generate the delay selection input.
4. The system of claim 1, wherein the predicted user reaction data
includes a patience index and the price optimization module
determines the time-dependent price data based on the patience
index.
5. The system of claim 1, wherein the user profiling module
computes a delay tolerance, relative to price sensitivity, of each
application on each mobile or fixed device.
6. The system of claim 1, wherein the user interface module
includes an auto-pilot mode, configured to make each decision on
time-deferral based on user specified parameters.
7. The system of claim 6, wherein the auto-pilot decisions are made
partially on the user devices and partially on network operators'
devices.
8. The system of claim 1, wherein the network traffic and user
reaction data are collected on the user devices, or on network
operator devices, or a combination of both types of devices.
9. The system of claim 1 wherein the price-optimization
computational module is configured to: allow network operators to
dynamically, over a plurality of possible timescales, adjust the
price charged for each unit of data traffic based on user
preferences, time of day, congestion conditions in historical
records and current conditions, and application needs; the user
interface module is configured to allow users to see, understand,
and respond with decisions of deferring an application or not, to
the dynamically adjusted prices with the help of visualization,
recommendation, prediction, and automatic agents that take into
account both price sensitivity and delay tolerance of each
application at each time.
10. The system of claim 1 wherein the price-optimization
computational module, user profiling module, user interface module
and network measurement module are run iteratively.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to earlier filed U.S.
Provisional Application No. 61/604,900 which was filed on Feb. 29,
2012 and U.S. patent application Ser. No. 13/780,941 filed Feb. 28,
2013, both of which are incorporated herein in their entirety.
TECHNICAL FIELD
[0002] The subject matter described herein relates generally to the
field of communication and computer networking, wireless systems
and networks, network economics and human computer interactions,
and information retrieval and data analytics.
BACKGROUND
[0003] The subject matter discussed in the background section
should not be assumed to be prior art merely as a result of its
mention in the background section. Similarly, a problem mentioned
in the background section or associated with the subject matter of
the background section should not be assumed to have been
previously recognized in the prior art. The subject matter in the
background section merely represents different approaches, which in
and of themselves may also correspond to embodiments of the claimed
subject matter.
[0004] Recent years have witnessed dramatic surges in bandwidth
demand. According to the Cisco Visual Networking Index, wireless
Internet bandwidth demand will increase at a compound rate of 108%
over the next five years. Relying solely on technology developments
such as long term evolution (LTE) and WiMAX to increase the supply
of wireless capacity is no longer viable going forward in the next
decade. Pricing innovations must also be considered to regulate
demand.
[0005] Many Internet Service Providers (ISPs), both wireless and
wireline broadband access providers such as AT&T and Comcast,
address the problem of growing bandwidth demands by using
usage-based pricing. Yet pricing based just on monthly bandwidth
usage still leaves a timescale mismatch: ISP revenue is based on
monthly usage, but peak-hour congestion dominates its cost
structure. Ideally, ISPs would like bandwidth consumption to be
spread evenly over all the hours of the day.
[0006] To solve the problem of congestion, some ISPs have been
experimenting with different pricing schemes for voice traffic.
There are two such schemes in practice, time-dependent pricing and
congestion-dependent pricing. Time-dependent pricing for voice
calls is in use by some ISPs in India, while congestion-dependent
pricing for voice traffic is used by MTN in Africa. However,
neither time-dependent nor congestion-dependent pricing for data
traffic has been used.
[0007] Time-dependent pricing for data traffic is the subject
matter of this invention, which provides a system and methods to
enable such pricing. Time-dependent Usage-based Broadband Price
Engineering (TUBE) is a term that will be used in the following to
denote such a system. Described herein are theory, algorithms, and
a full system implementation for this new pricing system and
methods.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] A more detailed understanding may be obtained from the
following description, given by way of example in conjunction with
the accompanying drawings herein:
[0009] FIG. 1 shows a sample trace of data usage over one day. The
double arrows indicate a large discrepancy between the peak and
average traffic over a day--there is significant opportunity for
the traffic profile to become more even if usage is shifted from
peak to "valley" (lower usage) periods.
[0010] FIG. 2 shows an estimate of how long users are willing to
wait for different types of traffic, given a 30% discount. Nearly
23% of traffic by volume can be delayed 1 hour, while only 2.8%
(mostly download traffic) can wait 24 hours.
[0011] FIG. 3 shows an overall schematic of the Time-dependent
Usage-based Broadband price Engineering (TUBE) system architecture,
with the four main system components of user behavior estimation,
price calculation, a user interface, and network measurement.
[0012] FIG. 4 shows the overall architecture of the TUBE system,
including the main modules of the TUBE Optimizer and TUBE User
Interface. The architecture incorporates the four system modules
from FIG. 3's overall schematic, including more sub-modules within
the user interface component.
[0013] FIG. 5 shows a TUBE GUI on an iPhone, where iPhone users can
view the past and future prices (1201) as well as their past usage
(1202), set delay tolerances for different applications (1203), and
manually schedule applications to be blocked at different times of
the day (1204).
[0014] FIG. 6 shows a flowchart of the price computation and user
behavior estimation algorithm. The algorithm is initialized with
randomly chosen prices, and a corresponding estimate of users'
willingness to wait for different types of traffic is then
computed. Day-ahead prices are then computed indefinitely, with the
estimated willingness to wait updated at the end of each day.
Dashed boxes represent decision points and arrows represent the
sequence of events.
[0015] FIG. 7 shows the implementation modules for the server-side
(TUBE Optimizer) part of the system architecture shown in FIG. 4.
Boxes represent different modules, and arrows show the data flow
between the different modules. The modules shown are one possible
implementation of the TDP system. Different implementations might
omit some modules or add others to accommodate the unique needs and
software capabilities of different TDP deployments.
[0016] FIG. 8 shows the implementation modules for the user
interface (TUBE User Interface) part of the system architecture
shown in FIG. 4. As in FIG. 7, boxes represent different modules,
and arrows represent data flow with the user interface. The modules
shown are one possible implementation of the user interface
component. Different implementations might omit some modules or add
others to accommodate the unique needs and software capabilities of
different TDP deployments.
[0017] FIG. 9 shows a simulation of time-dependent pricing's effect
on the usage trace over one day. The solid curve of baseline
traffic without time-dependent pricing clearly has a higher peak
traffic and lower "valley" traffic than the dashed curve of usage
with time-dependent pricing, showing TDP's effect on reducing the
peak-to-average ratio of the traffic pattern over the day.
[0018] FIG. 10 shows the discounts offered in different periods of
the day to achieve the traffic curve in FIG. 9. The discounts
during hours with heavier traffic (14:00 and later) are zero,
reflecting the heavier traffic; in contrast, more discounts are
offered in the early morning hours, when traffic is lighter. A
comparison with FIG. 8 shows that as expected, traffic is shifted
from heavier hours, when the discount is zero, to lighter hours
when a discount is offered.
[0019] FIG. 11 shows a TDP simulation over multiple days. The top
graph shows the traffic before (green) and after (red)
time-dependent pricing; the red curve is visibly flatter than the
green one, indicating that traffic has shifted from peak to valley
times. The corresponding time-dependent prices offered are shown in
the bottom graph; the prices adapt as the baseline traffic before
TDP changes from day to day.
DETAILED DESCRIPTION
[0020] TUBE differs from existing state-of-the-art mechanisms in
many ways. For example, TUBE is a pricing mechanism that addresses
the problem of peak-demand in the context of data as opposed to
just voice. It combines the ideas of TDP (time-dependent pricing)
and usage-based pricing into a coherent pricing scheme with full
system implementation.
[0021] Furthermore, the existing practices fall short of providing
a complete solution that does a fine-grained analysis of the user
behavior, traffic usage patterns, automation of user response to
dynamic pricing etc., while TUBE incorporates all these
features.
[0022] TUBE is a complete, novel system that is based on
mathematical rigor, sophisticated theory and algorithms, and
provides an end-to-end solution for pricing. It helps to realize
the full potential of dynamic pricing that is beneficial to both
users and service providers by adjusting the prices in response to
their usage behaviors.
[0023] TUBE also makes it easy to ISPs to provide much cheaper data
plans and can be used in developing wireless backhauls for
providing broadband services in rural areas.
[0024] TUBE is a system for creating innovative wireless data plans
to enable time-dependent pricing and congestion management. ISPs
that adopt TUBE will be able charge users based on not just how
much bandwidth they consume but also when they do so. The system
allows service providers to offer optimized prices that vary over
different times of the day and provides incentives to users for
shifting their traffic demand to periods when the prices are
cheaper. This benefits both parties involved; the service providers
can reduce their peak bandwidth demand and users get to save money
by deferring their high bandwidth, delay tolerant application
sessions.
[0025] There are three key immediate applications of TUBE: 1) As a
pricing system for time-dependent usage-based broadband data plans;
2) Reducing bandwidth requirements in rural back hauling; and 3) As
a congestion management tool for improving cellular network
performance, including 3G and 4G LTE networks.
[0026] The TUBE system may be extended to implement time-dependent
pricing for voice. Additionally, TUBE may be used to offer
ultra-affordable data plans or Ultra-Affordable Plans (UAPs). The
key idea is to accommodate UAP users by reducing the timescale of
TUBE's price optimization and using automation of user responses to
leverage the presence of "flashy whitespaces" in the air. The
flashy whitespaces, which appear even in congested spectrum, are
periods of time when the bandwidth becomes available. Therefore,
users who are somewhat delay tolerant will need to pay very little
by using UAP data plans. FIG. 1 shows examples of variations in
bandwidth demand over time.
[0027] The TUBE system leverages the rigorous theory of
optimization. It charges users based on not just "how much"
bandwidth is consumed but also "when" it is consumed. TDP is a
pricing innovation that is uniquely positioned to help ISPs meet
the challenges of growing user demand by spreading out the
congestion hour traffic more evenly throughout the day, thus
regulating bandwidth consumption and reducing the burden of
over-provisioning for peak hour demand for wireless operators.
[0028] As a pricing strategy, TDP also sits lower on the radar
screen of network neutrality scrutiny, as it does not differentiate
based on traffic type, protocol, or user class.
[0029] The TUBE Architecture overview is shown FIG. 4. TUBE
consists of the TUBE Optimizer and TUBE User Interface. TUBE
Optimizer is installed on ISP servers; it measures the
user-generated traffic and determines the prices offered to the
users using our advanced optimization algorithm. It consists of a
measurement engine to monitor user's traffic usage, a profiling
engine to estimate user's delay tolerance for each traffic class,
and a price optimization engine to calculate the optimal prices and
publish it to users.
[0030] TUBE User Interface consists of a GUI, a profiler, and a
recommendation engine, which run locally on the user's mobile
device. TUBE GUI presents users with an interface to see their
bandwidth usage and prices offered by the ISP that also takes user
inputs. The profiling engine learns the user's usage behavior,
which is then used by the recommendation engine to suggest deferral
of certain application sessions to a later time when the prices are
lower. Examples of TUBE GUI are shown in FIG. 5.
[0031] TUBE offers several competitive advantages. A
cost-effective, high-margin, and easy-to-deploy tool for
cost-savings for wireless and broadband ISPs. TUBE can reduce ISPs
costs from peak data demands, lower the cost of resource
investments in rural backhauls, and enable cheaper data plans for
low-income users. More generally, a very timely technology in the
context of the growing momentum among ISPs in using pricing as a
network management tool.
[0032] In a possible embodiment, the paper "TUBE: Time-dependent
pricing for mobile data," by Sangtae Ha, Soumya Sen, Carlee
Joe-Wong, Youngbin Im, and Mung Chiang, in Proceedings of ACM
SIGCOMM August 2012, presents a practical system for an end-to-end
solution in pricing innovation, complete with algorithms, prototype
implementation, and field trials. FIGS. 7 and 8 show software
modules for the prototype implementation.
[0033] In the above paper referenced, the steps involved are
creation of an analytical framework for optimal pricing for
time-dependent usage-based broadband pricing, implementing the
entire system by developing the software capability for generating
the optimal price information, developing algorithm for user
profiling and recommendation system to help user decisions, and
creating graphical user interfaces for our TUBE application that
will run on the user's handset.
[0034] This system for implementing time-dependent usage-based
broadband price engineering has significant commercial value for
service providers. This system will be used in broadband pricing by
ISPs and for offering low-cost data plans. Additionally, it may
also be used by ISPs to reduce capacity investments needed to
create wireless backhaul for providing broadband services in rural
areas, where deploying traditional wired networks is not
economically feasible due to low population density.
[0035] This invention allows ISPs to reduce the peak-usage in their
network and provides users a way to reduce their monthly bills by
shifting their demand to low-priced and low-congested time periods.
These benefits that TUBE provides to ISPs and users give it a
distinct advantage over existing flat-rate fees, usage-based
charges, and dynamic tariff schemes that are in practice today.
TUBE also allows for creating low-cost data plans and reducing ISP
costs of creating wireless backhauls in rural areas, which are not
directly feasible with any of the existing schemes.
[0036] In general, charging different prices for Internet access at
different times induces users to spread out their bandwidth
consumption across times of the day. The questions are: is it
feasible and how much benefit can it bring? This invention develops
an efficient method to compute the cost-minimizing time-dependent
prices for an Internet service provider (ISP), using both a static
session-level model and a dynamic session model with stochastic
arrivals. Our trial results demonstrate that optimal prices, which
"reward" users for deferring their sessions, roughly correlate with
demand in each period, and that changing prices based on real-time
traffic estimates may significantly reduce ISP cost. The degree to
which traffic is evened out over times of the day depends on the
time- sensitivity of sessions, cost structure of the ISP, and
amount of traffic not subject to time- dependent prices.
Simulations of this effect are shown in FIGS. 9-11.
[0037] The TUBE system architecture involves a price optimization
unit, user profiler, recommendation system, and formats for
interchanging price and usage information between user's end device
and ISP's servers. The TUBE system allows ISPs to reduce the
peak-usage in their network and provides a way for ISPs to offer a
low-cost data plans.
[0038] Additional disclosure is contained in "Time-Dependent
Broadband Pricing: Feasibility and Benefits" Carlee Joe-Wong,
Sangtae Ha, Mung Chiang, 2011 31st International Conference on
Distributed Computing Systems (ICDCS), 2011 IEEE, pp288-298, which
is incorporated herein in its entirety as if fully set forth. The
references cited throughout this application including any
appendices are incorporated for all purposes apparent herein and in
the references themselves as if each reference was fully set forth.
For the sake of presentation, specific ones of these references are
cited at particular locations herein and in other references. A
citation of a reference at a particular location indicates a manner
or manners in which the teachings of the reference are
incorporated. However, a citation of a reference in a particular
location does not limit the manner in which all of the teachings of
the cited reference are incorporated for all purposes.
[0039] Although features and elements are described above in
particular combinations, each feature or element can be used alone
without the other features and elements or in various combinations
with or without other features and elements. The methods or flow
charts provided herein may be at least partially implemented in a
computer program, software, or firmware incorporated in a
computer-readable storage medium for execution by a general purpose
computer or a processor. For example, the user device and the ISP
server may include one or more processors configured with the code
to enable the functionality disclosed above. Examples of
computer-readable storage mediums include non-transitory devices
such as read only memory (ROM), random access memory (RAM),
registers, cache memory, semiconductor memory devices, magnetic
media such as internal hard disks and removable disks,
magneto-optical media, and optical media such as CD-ROM disks, and
digital versatile disks (DVDs). Suitable processors include, by way
of example, a general purpose processor, a special purpose
processor, a conventional processor, a digital signal processor
(DSP), a plurality of microprocessors, one or more microprocessors
in association with a DSP core, a microcontroller, Application
Specific Integrated Circuits (ASICs), Field Programmable Gate
Arrays (FPGAs) circuits, any other type of integrated circuit (IC),
and/or a state machine.
[0040] It is understood, therefore, that the invention is not
limited to the particular embodiments disclosed, but is intended to
cover all modifications which are within the spirit and scope of
the invention as defined by appended claims, the above description,
and any appendices.
* * * * *